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Deep Learning for Multi-Sensor Earth Observation
- 1st Edition - February 1, 2025
- Editor: Sudipan Saha
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 6 4 8 4 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 6 4 8 5 - 6
Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insigh… Read more
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Request a sales quoteDeep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.
Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.
Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.
- Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality
- Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences
- Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice
Remote sensing and geoscience professors, researchers, students and professionals in industry.
Section 1: Introduction to Multi-Sensor Data and Artificial Intelligence
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
- No. of pages: 350
- Language: English
- Edition: 1
- Published: February 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443264849
- eBook ISBN: 9780443264856
SS
Sudipan Saha
Sudipan Saha is currently an Assistant Professor at Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India. Previously, he worked as a postdoctoral researcher at the Artificial Intelligence for Earth Observation (AI4EO) Lab, Technical University of Munich, Germany (2020-2022). He received a Ph.D. degree in Information and Communication Technologies from the University of Trento and Fondazione Bruno Kessler (FBK), Trento, Italy in 2020, working with Dr. Francesca Bovolo and Prof. Lorenzo Bruzzone. He is the recipient of FBK Best Student Award 2020. Previously, he obtained the M.Tech. degree in Electrical Engineering from IIT Bombay, Mumbai, India in 2014 where he is recipient of Postgraduate Color. He worked as an Engineer with TSMC Limited, Hsinchu, Taiwan, from 2015 to 2016. His research interests are related to multi-temporal and multi-sensor satellite image analysis, uncertainty quantification, deep learning, and climate change.
Affiliations and expertise
Assistant Professor, Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India